Other approaches average frequency first and then summarize time.
PANNs'[1]} CNN14 model first averages the frequencyhttps://github.com/qiuqiangkong/audioset_tagging_cnn/blob/master/pytorch/models.py#L215, and applies a temporal pooling, which we refer to as temporal mean+max pooling hereafter. It first applies both temporal mean- and max-pooling in parallel, then sums both embeddings as the output embedding.
Fonseca et al. [2]} also used frequency average pooling first and then applied temporal max pooling.
Ford et al.[3]} proposed to add an attention module for summarizing the output of frequency average pooling.
